CN102608587B - Air mobile target detection method based on nonlinear least square - Google Patents

Abstract

The invention provides an air mobile target detection method based on nonlinear least square. The method comprises the following steps of: 1) performing clutter rejection on the total echo data received by a radar; 2) determining the two-dimensional search range of a target parameter; 3) constructing a target signal model; and 4) constructing a cost function, and estimating the parameter result and the like. The air mobile target detection method based on nonlinear least square, provided by the invention, reconstructs a target signal in a parameter search range by use of the thought of the nonlinear least square algorithm, and then matches the target signal with the data after clutter rejection by use of the nonlinear least square algorithm to obtain the parameter estimation result of the target. The method provided by the invention has the advantages of strong target detection ability, high accuracy of parameter estimation result, better estimation performance and the like, and particularly can obtain a good parameter estimation result even under the condition of limited pulse points.

Description

Air mobile object detection method based on non-linear least square

Technical field

The invention belongs to the Radar Signal Processing Technology field, particularly relate to a kind of air mobile object detection method based on non-linear least square (NLS).

Background technology

The high-altitude motion platform of take is more much bigger than ground radar as the visual range of the airborne early warn ing radar of carrier, is one of most important Military Sensors on modern battlefield.But, due to it, look duty under being in, therefore be faced with the ground more complicated than ground radar (sea) clutter problem, make detection and parameter estimation to target become very difficult.Space-time adaptive is processed (Space-Time Adaptive Processing, STAP) be that a kind of effective airborne radar land clutter suppresses means, traditional STAP method is all that hypothesis is at (Coherent Processing Interval of relevant processing time, CPI) internal object Echo Doppler Frequency constant (being that target moves with uniform velocity), but when target is carried out speed change, during the maneuvering flights such as turning, it can change in time a CPI internal object Echo Doppler Frequency, Doppler namely occurs to walk about, make traditional STAP method coherent build-up properties greatly descend, thereby cause the target detection ability to descend.

Target echo signal is linear frequency modulation (Linear Frequency Modulation, LFM) signal when maneuvering target is done uniformly accelerated motion.Detection and parameter estimation to the LFM signal have the classic algorithm such as maximal possibility estimation (Maximum Likelihood, ML) and time frequency analysis.Wherein the ML method is the most effective a kind of method of estimation, its estimated accuracy is very high, to the estimation variance of parameter close to the Cramer-Rao lower bound, but it is very large that its shortcoming is operand, and the parameter estimation model of the method carries out under the white Gaussian noise environment, and in actual conditions, noise is coloured noise often, so the ML method is unfavorable for processing in real time and Project Realization in actual conditions.While utilizing Time-Frequency Analysis Method to estimate the maneuvering target parameter, need more sampling number, therefore the method is applied relatively extensively (because ground radar and SAR transponder pulse number are more) in ground radar and synthetic-aperture radar (Synthetic Aperture Radar, SAR).And when the pulse repetition rate one of airborne early warn ing radar regularly, more sampling number means that CPI lengthens, this can cause the range walk of clutter and target, thereby to subsequent treatment, bring larger difficulty, therefore directly utilize Time-Frequency Analysis Method to detect the air mobile target and there will be the poor problem of estimated accuracy.

Summary of the invention

In order to address the above problem, the object of the present invention is to provide a kind of object detection method of air mobile based on non-linear least square that can improve Parameter Estimation Precision.

In order to achieve the above object, the object detection method of the air mobile based on non-linear least square provided by the invention comprises the following step carried out in order:

1) total echo data radar received carries out the S1 stage of clutter inhibition;

2) determine the S2 stage of target component two-dimensional search scope;

3) the S3 stage of structure echo signal model;

4) structure cost function, the S4 stage of estimated parameter result.

In step 1) in, the method that described total echo data that radar is received carries out the clutter inhibition is to utilize subspace projection technique to carry out the clutter inhibition to the echo data that radar receives, be about in orthogonal subspaces that cell data to be detected projects to the clutter subspace, obtain after projection without the clutter data.

In step 2) in, the method of described definite target component two-dimensional search scope is to utilize to separate fast initial velocity and the acceleration that line tune method estimates echo signal roughly, determine the two-dimensional search scope of a target component, for next step structure echo signal model is prepared.

In step 3) in, described structure echo signal model method is to utilize parameter in the parameter search scope that S2 determines in the stage to re-construct the echo signal model of phased-array radar.

In step 4) in, described structure cost function, the method of estimating target parameter result is according to Nonlinear Least-Square Algorithm, the data after the echo signal model of S3 reconstruct in the stage and clutter inhibition to be complementary, the structure cost function, make the data after itself and clutter suppress have minimum " quadratic sum " distance, parameter corresponding to search cost function minimum is as estimated result.

Air mobile based on non-linear least square object detection method provided by the invention is the thought of utilizing Nonlinear Least-Square Algorithm, reconstruct echo signal in a parameter search scope, then adopt the data after Nonlinear Least-Square Algorithm suppresses itself and clutter to mate, and then obtain the parameter estimation result of target.The inventive method has target detection ability and the parameter estimation advantages such as precision is high as a result, estimated performance is better, especially, in the situation that pulse is counted limitedly, still can obtain good parameter estimation result.

The accompanying drawing explanation

Fig. 1 is the object detection method of the air mobile based on non-linear least square process flow diagram provided by the invention.

Fig. 2 is the power spectrum of total echo before clutter suppresses.

Fig. 3 is the power spectrum of conventional method after clutter suppresses.

Fig. 4 is the power spectrum after the inventive method is processed.

Fig. 5 is to power spectrum before and after acceleration compensation.

Fig. 6 (a) is that the initial velocity root-mean-square error is with the signal to noise ratio (S/N ratio) change curve.

Fig. 6 (b) is that the acceleration-root-mean square error is with the signal to noise ratio (S/N ratio) change curve.

Embodiment

Below in conjunction with the drawings and specific embodiments, the object detection method of the air mobile based on non-linear least square provided by the invention is elaborated.

Fig. 1 is the object detection method of the air mobile based on non-linear least square process flow diagram provided by the invention.All operations were wherein is all in the airborne computer system of core, to complete take computing machine, and the main body of operation is airborne computer system.

As shown in Figure 1, the object detection method of the air mobile based on non-linear least square provided by the invention comprises the following step carried out in order:

1) total echo data radar received carries out the S1 stage of clutter inhibition: this stage is to utilize subspace projection technique to carry out the clutter inhibition to the echo data that radar receives, and then enters next step S2 stage;

In this stage, utilize subspace projection technique as follows to the concrete grammar that echo data carries out the clutter inhibition: cell data to be detected is projected in the orthogonal subspaces of clutter subspace, obtain after projection without the clutter data.Clutter plus noise covariance matrix is:

R＝E{(x _c+x _n)(x _c+x _n) ^H}＝R _c+R _n (1)

In formula, R _cMean clutter covariance matrix, R _nIt is the noise item covariance matrix.R is carried out to Eigenvalues Decomposition, can obtain:

$R=\underset{m=1}{\overset{M}{\mathrm{\&Sigma;}}}{\mathrm{\&lambda;}}_m{u}_m{u}_m^H\&ap;\underset{m=1}{\overset{Q}{\mathrm{\&Sigma;}}}{\mathrm{\&lambda;}}_m{u}_m{u}_m^H+{\mathrm{\&sigma;}}_{\mathrm{<figure-callout\; id="2"\; label="n"\; filenames="HDA0000141182030000012.png"\; state="\{\{state\}\}">n</figure-callout>}}^2\underset{m=Q+1}{\overset{M}{\mathrm{\&Sigma;}}}{u}_m{u}_m^H---\left(2\right)$

Wherein, λ _m(m=1 ... Q) be Q large eigenwert, Q is clutter eigenwert number, and remaining M-Q eigenwert equates, is

u _m(m=1 ... Q) be m the clutter subspace that large eigenwert characteristic of correspondence vector is opened, be designated as U _C=spam{u ₁..., u _Q.The projection matrix of its orthogonal complement space is:

$P_C^{\&perp;}=U_C^{\&perp;}{\left(U_C^{\&perp;}\right)}^H---\left(3\right)$

As can be known by above-mentioned derivation, after projection, without the clutter data be

$x_{\mathrm{proj}}=P_C^{\&perp;}x---\left(4\right)$

Due to usually

And determine that clutter subspace dimension is more complicated, so we use R ^-1Replace

Clutter reduction, namely

x _proj＝R ^-1x (5)

2) determine the S2 stage of target component two-dimensional search scope: this stage is utilized and separates fast initial velocity and the acceleration that line tune method estimates echo signal roughly, determines the two-dimensional search scope of a target component, then enters next step S3 stage;

In this stage, in order to reduce the calculated amount of the inventive method, at first initial velocity and the acceleration of target are carried out to rough estimate, form a parameter search scope, for next step essence, estimate and prepare.Separate fast line tune method and add the numerous and diverse two-dimensional search of sinusoidal signal frequency estimation replacement maximal possibility estimation twice with an auto-correlation computation, saved operand, realized the quick estimation of LFM parameter.Therefore can utilize quick solution line to adjust method to estimate roughly initial velocity and the acceleration of echo signal, the two-dimensional search scope of determining a target component (is common method owing to separating fast line tune method, those skilled in the art understand, therefore no longer narration here).

3) the S3 stage of structure echo signal model: utilize parameter in the parameter search scope that S2 determines in the stage to re-construct the echo signal model of phased-array radar;

In this stage, the concrete grammar of described structure echo signal model is as follows.As can be known by the S1 phase analysis, the data after clutter suppresses also comprise echo signal, therefore, and can echo signal of reconstruct

$x_s=b_t\&CenterDot;a(u_t,{\mathrm{\&omega;}}_t)=b_t\&CenterDot;a\left({\mathrm{\&omega;}}_t\right)\&CircleTimes;a\left(u_t\right)$

$=b_t\&CenterDot;{[1,e^{j(2\mathrm{\&pi;}\&CenterDot;1\&CenterDot;\frac{2v}{{\mathrm{\&lambda;f}}_r}+\mathrm{\&pi;}\&CenterDot;1\&CenterDot;\frac{2a}{{{\mathrm{\&lambda;f}}_r}^2})},\&CenterDot;\&CenterDot;\&CenterDot;,e^{j(2\mathrm{\&pi;}\&CenterDot;(K-1)\&CenterDot;\frac{2v}{{\mathrm{\&lambda;f}}_r}+\mathrm{\&pi;}\&CenterDot;{(K-1)}^2\&CenterDot;\frac{2a}{{{\mathrm{\&lambda;f}}_r}^2})}]}^T\&CircleTimes;{[1,e^{\frac{\mathrm{<figure-callout\; id="2"\; label="j"\; filenames="HDA0000141182030000012.png"\; state="\{\{state\}\}">j</figure-callout>}2\mathrm{\&pi;}\&CenterDot;1\&CenterDot;d\cos {\mathrm{\&psi;}}_t}{\mathrm{\&lambda;}}},\&CenterDot;\&CenterDot;\&CenterDot;,{\mathrm{<figure-callout\; id="2"\; label="e\; j"\; filenames="HDA0000141182030000012.png"\; state="\{\{state\}\}">e</figure-callout>}}^{\frac{j}{}}]}^T---\left(6\right)$

Wherein, b _tFor the target echo amplitude, the spatial domain steering vector For N * 1 dimensional vector, time domain steering vector

For K * 1 dimensional vector, wherein comprise the unknown parameter of target, be respectively initial velocity v and acceleration a.

4) structure cost function, the S4 stage of estimated parameter result: the data after the echo signal model of S3 reconstruct in the stage and clutter inhibition are complementary according to Nonlinear Least-Square Algorithm, the structure cost function, parameter corresponding to search cost function minimum is as estimated result, and so far described testing process finishes.

In this stage, the concrete grammar of described structure cost function is as follows.Data after the echo signal model that S3 was constructed in the stage and clutter suppress are complementary, and utilize non-linear least square method to carry out parameter estimation, the structure cost function

$(\hat{v},\hat{a})=\arg \underset{(v,a)}{\min }\left[{\left|\right|x_{\mathrm{proj}}-x_s\left|\right|}^2\right]$ (7)

$=\arg \underset{(v,a)}{\min }\left[{\left|\right|x_{\mathrm{proj}}-b_t\&CenterDot;a(u_t,{\mathrm{\&omega;}}_t)\left|\right|}^2\right]$

Wherein, x _ProjFor the data after the clutter inhibition, x _sFor the echo signal after reconstruct, wherein comprise and treat estimated parameter.When top cost function was obtained minimum value, corresponding parameter, be estimated result, now the data x after the echo signal after reconstruct and clutter inhibition _ProjBetween have minimum " quadratic sum " distance.

Yet, utilize formula (7) while constructing cost function, also to need to know the amplitude b of echo signal _t, for fear of the estimation to the echo signal amplitude, can come with shortcut calculation initial velocity and the acceleration of estimating target.Due to a (u _t, ω _t) be the row full ranks, therefore [a (u _t, ω _t) ^HA (u _t, ω _t)] ^-1Exist, so the convenient form of the deployable one-tenth of cost function in formula (7):

$f(v,a)={[x_{\mathrm{proj}}-b_t\&CenterDot;a(u_t,{\mathrm{\&omega;}}_t)]}^H[x_{\mathrm{proj}}-b_t\&CenterDot;a(u_t,{\mathrm{\&omega;}}_t)]$

$={\{b_t-{[a{(u_t,{\mathrm{\&omega;}}_t)}^H\&CenterDot;a(u_t,{\mathrm{\&omega;}}_t)]}^{-1}\&CenterDot;a{(u_t,{\mathrm{\&omega;}}_t)}^H{\&CenterDot;x}_{\mathrm{proj}}\}}^H\&CenterDot;[a{(u_t,{\mathrm{\&omega;}}_t)}^H\&CenterDot;a(u_t,{\mathrm{\&omega;}}_t)]$ (8)

$\&CenterDot;\{b_t-{[a{(u_t,{\mathrm{\&omega;}}_t)}^H\&CenterDot;a(u_t,{\mathrm{\&omega;}}_t)]}^{-1}\&CenterDot;a{(u_t,{\mathrm{\&omega;}}_t)}^H\&CenterDot;x_{\mathrm{proj}}\}+x_{\mathrm{proj}}^H\&CenterDot;x_{\mathrm{proj}}$

$-x_{\mathrm{proj}}^H\&CenterDot;a(u_t,{\mathrm{\&omega;}}_t)\&CenterDot;{[a{(u_t,{\mathrm{\&omega;}}_t)}^H\&CenterDot;a(u_t,{\mathrm{\&omega;}}_t)]}^{-1}\&CenterDot;a{(u_t,{\mathrm{\&omega;}}_t)}^H\&CenterDot;x_{\mathrm{proj}}$

Can select b _tFirst that makes cost function f is zero, this shows the v that minimizes f, and a is:

$(\hat{v},\hat{a})=\arg \underset{(v,a)}{\min }\left[{\left|\right|x_{\mathrm{proj}}-b_t\&CenterDot;a(u_t,{\mathrm{\&omega;}}_t)\left|\right|}^2\right]$ (9)

$=\arg \underset{(v,a)}{\max }\{x_{\mathrm{proj}}^H\&CenterDot;a(u_t,{\mathrm{\&omega;}}_t)\&CenterDot;{[a{(u_t,{\mathrm{\&omega;}}_t)}^H\&CenterDot;a(u_t,{\mathrm{\&omega;}}_t)]}^{-1}\&CenterDot;a{(u_t,{\mathrm{\&omega;}}_t)}^H\&CenterDot;x_{\mathrm{proj}}\}$

Therefore, in actual applications, can utilize the cost function of formula (9) to carry out the estimating target parameter.Wherein, a (u _t, ω _t) wire vector while being the target empty of constructing, comprising the unknown parameter of target, see formula (6).Cost function formula (9) while obtaining maximal value corresponding parameter be estimated result.

The effect of the object detection method of the air mobile based on non-linear least square provided by the invention can further illustrate by following simulation result.

Emulated data is described: antenna array is the desirable even linear array of the positive side-looking of array number N=16, array element distance d=0.5 λ.Carrier aircraft speed is 120m/s, and the radar operation wavelength is 0.32m, and podium level is 10km, and distance by radar resolution is 20m, and pulse repetition rate is 1500Hz, relevant umber of pulse K=64, input signal-to-noise ratio SNR=0dB, the miscellaneous noise ratio CNR=50dB of processing.Maneuvering target is in detecting unit, is in 90 °, position angle and locates, and initial velocity is 24.01m/s, and acceleration is a=99.9m/s ², in experiment, the hypothetical target orientation is known.

Fig. 2 is the power spectrum of total echo before clutter suppresses.As shown in Figure 2, because signal to noise ratio is very low, signal is submerged in clutter fully.Fig. 3 is the power spectrum of conventional method after clutter suppresses, and clutter is suppressed although can find out has fallen, and target highlights, and because there is acceleration in target, there is certain broadening in it at Doppler domain, makes the follow-up parameter estimation difficulty that becomes.Fig. 4 is the power spectrum after the inventive method is processed, and namely utilizes estimated result to compensate the acceleration item, and now energy reassembles at Doppler domain, has improved target detection ability and Parameter Estimation Precision.

Fig. 5 is to power spectrum before and after acceleration compensation.Power spectrum when '---' in Fig. 5 and '-' are respectively cos ψ=0 in Fig. 3 and Fig. 4 (90 °, position angle), can find out the effect of energy accumulation before and after acceleration compensation more significantly, and the accumulative effect of visible the inventive method is best.

Table 1 illustrates the root-mean-square error of distinct methods estimated result, can find out, the parameter estimation of the inventive method precision as a result is the highest.

Under different signal to noise ratio (S/N ratio)s, the root-mean-square error of target initial velocity and acceleration estimation as shown in Figure 6, wherein Fig. 6 (a) be the initial velocity root-mean-square error with the signal to noise ratio (S/N ratio) change curve, Fig. 6 (b) for the acceleration-root-mean square error with the signal to noise ratio (S/N ratio) change curve.'-+-' be the estimated result to single array element data, ' zero-' is for carrying out the estimated result after incoherent accumulation to a plurality of array element data, ' *-' is for adopting the inventive method estimated result, '-*-' be CRB circle of maneuvering target parameter estimation, best by the estimated performance that relatively can find out the inventive method, near CRB circle, especially in the situation that low signal-to-noise ratio, its advantage is more obvious.

Table 1 distinct methods estimated result comparison sheet

	RMSE v(dB)	RMSE a(dB)
			Single array element estimated result	0.1323	16.7585
Incoherent accumulation estimated result	-3.1180	12.5140
			The inventive method estimated result	-6.7368	9.6089

Claims (1)

1. object detection method of the air mobile based on non-linear least square, it comprises the following step carried out in order:

1) total echo data radar received carries out the S1 stage of clutter inhibition;

2) determine the S2 stage of target component two-dimensional search scope;

It is characterized in that: described air mobile object detection method also comprises:

3) the S3 stage of structure echo signal model, method is to utilize parameter in the parameter search scope that S2 determines in the stage to re-construct the echo signal model of phased-array radar;

4) structure cost function, the S4 stage of estimated parameter result, method is according to Nonlinear Least-Square Algorithm, the data after the echo signal model of S3 reconstruct in the stage and clutter inhibition to be complementary, the structure cost function, make the data after itself and clutter suppress have minimum " quadratic sum " distance, parameter corresponding to search cost function minimum is as estimated result.

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